An Architecture to Support Learning-based Adaptation of Persistent Queries in Mobile Environments

Queries are frequently used by applications in dynamically formed mobile networks to discover and acquire information and services available in the surrounding environment. A number of inquiry strategies exist, each of which embodies an approach to disseminating a query and collecting results. The choice of inquiry strategy has different tradeoffs under different operating conditions. Therefore, it is beneficial to allow a query-based application to dynamically adapt its inquiry strategy to the changing environmental conditions. To promote development by non-expert domain programmers, we can automate the decision-making process associated with adapting the inquiry strategy. In this paper, we propose an architecture to support automated adaptative query processing for dynamic mobile environments. The decision-support module of our architecture relies on an instance-based learning approach to support context-aware adaptation of the inquiry strategy

Adaptive network protocols to support queries in dynamic networks

Recent technological advancements have led to the popularity of mobile devices, which can dynamically form wireless networks. In order to discover and obtain distributed information, queries are widely used by applications in opportunistically formed mobile networks. Given the popularity of this approach, application developers can choose from a number of implementations of query processing protocols to support the distributed execution of a query over the network. However, different inquiry strategies (i.e., the query processing protocol and associated parameters used to execute a query) have different tradeoffs between the quality of the query's result and the cost required for execution under different operating conditions. The application developer's choice of inquiry strategy is important to meet the application's needs while considering the limited resources of the mobile devices that form the network. We propose adaptive approaches to choose the most appropriate inquiry strategy in dynamic mobile environments. We introduce an architecture for adaptive queries which employs knowledge about the current state of the dynamic mobile network and the history of previous query results to learn the most appropriate inquiry strategy to balance quality and cost tradeoffs in a given setting, and use this information to dynamically adapt the continuous query's execution

Context Aware Computing for The Internet of Things: A Survey

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201

A service-oriented middleware for composing context aware mobile services

Recent advances in wireless networks and mobile devices have brought about new scenes for the provision of services to end-users. Besides traditional services, new ones may be provided that transparently adjust and adapt to the user context. The user would have more choice and flexibility if, besides using the services, he could also compose his own services in an ad-hoc way. This paper presents iCas, an architecture to create context-aware services on the fly and discusses its main components. Also an application scenario is briefly described

Composition of context aware mobile services using a semantic context model

Context-awareness has been regarded as an important feature for mobile services. However, only a few services are sensible to context and the features that are context-aware are still limited. Composition of Web services has received much interest in business-to-business or enterprise application, but not so much interest in business-to- consumer applications. This paper presents iCas, a novel architecture that enables the creation of context-aware services on the fly, and discusses its main components. We compare our approach with similar systems and point out the main differences and advantages. To explore context-awareness to support service composition, iCas uses SeCoM, a semantic model to represent context. The main parts of this model are explained as well the advantages of using a semantic model to represent context. We also describe the use of our approach in an university campus to provide pedagogical features and assist the socio-pedagogical interaction of various types of users

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges